
"""simple docstring"""


import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      '''bart'''
       UpperCamelCase_		:       List[str]				=      ['''past_key_values''']
       UpperCamelCase_		:       List[Any]				=      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






       def __init__(	self			:				str							,   UpperCAmelCase_			:				Optional[Any]=50_265							,   UpperCAmelCase_			:				Any=1_024							,   UpperCAmelCase_			:				List[Any]=12							,   UpperCAmelCase_			:				List[Any]=4_096							,   UpperCAmelCase_			:				Tuple=16							,   UpperCAmelCase_			:				List[Any]=12							,   UpperCAmelCase_			:				Union[str, Any]=4_096							,   UpperCAmelCase_			:				Union[str, Any]=16							,   UpperCAmelCase_			:				Union[str, Any]=0.0							,   UpperCAmelCase_			:				List[Any]=0.0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				List[str]=1_024							,   UpperCAmelCase_			:				Dict=0.1							,   UpperCAmelCase_			:				Optional[Any]=0.0							,   UpperCAmelCase_			:				int=0.0							,   UpperCAmelCase_			:				List[Any]=0.02							,   UpperCAmelCase_			:				Any=0.0							,   UpperCAmelCase_			:				Optional[int]=False							,   UpperCAmelCase_			:				Union[str, Any]=True							,   UpperCAmelCase_			:				int=3							,   UpperCAmelCase_			:				int=1							,   UpperCAmelCase_			:				Dict=0							,   UpperCAmelCase_			:				Union[str, Any]=2							,   UpperCAmelCase_			:				Optional[int]=True							,   UpperCAmelCase_			:				Dict=2							,   UpperCAmelCase_			:				int=2							,   **UpperCAmelCase_			:				Dict							,   )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		d_model
              UpperCamelCase       			=		encoder_ffn_dim
              UpperCamelCase       			=		encoder_layers
              UpperCamelCase       			=		encoder_attention_heads
              UpperCamelCase       			=		decoder_ffn_dim
              UpperCamelCase       			=		decoder_layers
              UpperCamelCase       			=		decoder_attention_heads
              UpperCamelCase       			=		dropout
              UpperCamelCase       			=		attention_dropout
              UpperCamelCase       			=		activation_dropout
              UpperCamelCase       			=		activation_function
              UpperCamelCase       			=		init_std
              UpperCamelCase       			=		encoder_layerdrop
              UpperCamelCase       			=		decoder_layerdrop
              UpperCamelCase       			=		classifier_dropout
              UpperCamelCase       			=		use_cache
              UpperCamelCase       			=		encoder_layers
              UpperCamelCase       			=		scale_embedding  # scale factor will be sqrt(d_model) if True

              super().__init__(
                  num_labels=UpperCAmelCase_							,   pad_token_id=UpperCAmelCase_							,   bos_token_id=UpperCAmelCase_							,   eos_token_id=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   decoder_start_token_id=UpperCAmelCase_							,   forced_eos_token_id=UpperCAmelCase_							,   **UpperCAmelCase_							,   )

              # ensure backward compatibility for BART CNN models
              if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		self.bos_token_id
                     warnings.warn(
                         f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                         "The config can simply be saved and uploaded again to be fixed."      )





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]      )

                     if self.use_past:
                            UpperCamelCase       			=		{0: "batch"}
                            UpperCamelCase       			=		{0: "batch", 1: "past_decoder_sequence + sequence"}
                     else:
                            UpperCamelCase       			=		{0: "batch", 1: "decoder_sequence"}
                            UpperCamelCase       			=		{0: "batch", 1: "decoder_sequence"}

                     if self.use_past:
                            self.fill_with_past_key_values_(UpperCAmelCase_							,   direction="inputs"      )
              elif self.task == "causal-lm":
                     # TODO: figure this case out.
                     UpperCamelCase       			=		OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ]      )
                     if self.use_past:
                            UpperCamelCase			,							UpperCamelCase       			=		self.num_layers
                            for i in range(UpperCAmelCase_      ):
                                   UpperCamelCase       			=		{0: "batch", 2: "past_sequence + sequence"}
                                   UpperCamelCase       			=		{0: "batch", 2: "past_sequence + sequence"}
              else:
                     UpperCamelCase       			=		OrderedDict(
                         [
                             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                             ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                             ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                         ]      )

              return common_inputs






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		super().outputs
              else:
                     UpperCamelCase       			=		super(UpperCAmelCase_							,   self      ).outputs
                     if self.use_past:
                            UpperCamelCase			,							UpperCamelCase       			=		self.num_layers
                            for i in range(UpperCAmelCase_      ):
                                   UpperCamelCase       			=		{0: "batch", 2: "past_sequence + sequence"}
                                   UpperCamelCase       			=		{0: "batch", 2: "past_sequence + sequence"}
              return common_outputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              # Generate decoder inputs
              UpperCamelCase       			=		seq_length if not self.use_past else 1
              UpperCamelCase       			=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
              UpperCamelCase       			=		dict(**UpperCAmelCase_							,   **UpperCAmelCase_      )

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
                     else:
                            import torch
                     UpperCamelCase			,							UpperCamelCase       			=		common_inputs["input_ids"].shape
                     UpperCamelCase       			=		common_inputs["decoder_input_ids"].shape[1]
                     UpperCamelCase			,							UpperCamelCase       			=		self.num_attention_heads
                     UpperCamelCase       			=		(
                         batch,
                         num_encoder_attention_heads,
                         encoder_seq_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )
                     UpperCamelCase       			=		decoder_seq_length + 3
                     UpperCamelCase       			=		(
                         batch,
                         num_decoder_attention_heads,
                         decoder_past_length,
                         self._config.hidden_size // num_decoder_attention_heads,
                     )

                     UpperCamelCase       			=		torch.cat(
                         [common_inputs["decoder_attention_mask"], torch.ones(UpperCAmelCase_							,   UpperCAmelCase_      )]							,   dim=1      )

                     UpperCamelCase       			=		[]
                     # If the number of encoder and decoder layers are present in the model configuration, both are considered
                     UpperCamelCase			,							UpperCamelCase       			=		self.num_layers
                     UpperCamelCase       			=		min(UpperCAmelCase_							,   UpperCAmelCase_      )
                     UpperCamelCase       			=		max(UpperCAmelCase_							,   UpperCAmelCase_      ) - min_num_layers
                     UpperCamelCase       			=		"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                     for _ in range(UpperCAmelCase_      ):
                            common_inputs["past_key_values"].append(
                                (
                                    torch.zeros(UpperCAmelCase_      ),
                                    torch.zeros(UpperCAmelCase_      ),
                                    torch.zeros(UpperCAmelCase_      ),
                                    torch.zeros(UpperCAmelCase_      ),
                                )      )
                     # TODO: test this.
                     UpperCamelCase       			=		encoder_shape if remaining_side_name == "encoder" else decoder_shape
                     for _ in range(UpperCAmelCase_							,   UpperCAmelCase_      ):
                            common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase_      ), torch.zeros(UpperCAmelCase_      ))      )
              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              if self.use_past:
                     if not is_torch_available():
                            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
                     else:
                            import torch
                     UpperCamelCase			,							UpperCamelCase       			=		common_inputs["input_ids"].shape
                     # Not using the same length for past_key_values
                     UpperCamelCase       			=		seqlen + 2
                     UpperCamelCase			,							UpperCamelCase       			=		self.num_layers
                     UpperCamelCase			,							UpperCamelCase       			=		self.num_attention_heads
                     UpperCamelCase       			=		(
                         batch,
                         num_encoder_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // num_encoder_attention_heads,
                     )

                     UpperCamelCase       			=		common_inputs["attention_mask"].dtype
                     UpperCamelCase       			=		torch.cat(
                         [common_inputs["attention_mask"], torch.ones(UpperCAmelCase_							,   UpperCAmelCase_							,   dtype=UpperCAmelCase_      )]							,   dim=1      )
                     UpperCamelCase       			=		[
                         (torch.zeros(UpperCAmelCase_      ), torch.zeros(UpperCAmelCase_      )) for _ in range(UpperCAmelCase_      )
                     ]
              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              # Copied from OnnxConfig.generate_dummy_inputs
              # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCamelCase       			=		compute_effective_axis_dimension(
                  UpperCAmelCase_							,   fixed_dimension=OnnxConfig.default_fixed_batch							,   num_token_to_add=0      )

              # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
              UpperCamelCase       			=		tokenizer.num_special_tokens_to_add(UpperCAmelCase_      )
              UpperCamelCase       			=		compute_effective_axis_dimension(
                  UpperCAmelCase_							,   fixed_dimension=OnnxConfig.default_fixed_sequence							,   num_token_to_add=UpperCAmelCase_      )

              # Generate dummy inputs according to compute batch and sequence
              UpperCamelCase       			=		[" ".join([tokenizer.unk_token]      ) * seq_length] * batch_size
              UpperCamelCase       			=		dict(tokenizer(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_      )      )
              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				PreTrainedTokenizer							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional[TensorType] = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                         UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   seq_length=UpperCAmelCase_							,   is_pair=UpperCAmelCase_							,   framework=UpperCAmelCase_      )

              elif self.task == "causal-lm":
                     UpperCamelCase       			=		self._generate_dummy_inputs_for_causal_lm(
                         UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   seq_length=UpperCAmelCase_							,   is_pair=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              else:
                     UpperCamelCase       			=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                         UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   seq_length=UpperCAmelCase_							,   is_pair=UpperCAmelCase_							,   framework=UpperCAmelCase_      )

              return common_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				List[Any]							,   UpperCAmelCase_			:				Union[str, Any]      )->	Dict:







              """simple docstring"""
              if self.task in ["default", "seq2seq-lm"]:
                     UpperCamelCase       			=		super()._flatten_past_key_values_(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              else:
                     UpperCamelCase       			=		super(UpperCAmelCase_							,   self      )._flatten_past_key_values_(
                         UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )







"""simple docstring"""


def 						lowerCamelCase__      (				UpperCAmelCase_ = 60_08_51_47_51_43					)->				int:







       """simple docstring"""

       try:
              UpperCamelCase       			=		int(UpperCAmelCase_					)
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."					)
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."					)
       UpperCamelCase       			=		2
       UpperCamelCase       			=		0
       if n == 2:
              return 2
       while n > 2:
              while n % i != 0:
                     i += 1
              UpperCamelCase       			=		i
              while n % i == 0:
                     UpperCamelCase       			=		n // i
              i += 1
       return int(UpperCAmelCase_					)


if __name__ == "__main__":
      print(F'''{solution() = }''')





